mlaico / awesome-deep-learning Goto Github PK

1. [Ilya Sutskever: A Brief Overview of Deep Learning] (http://yyue.blogspot.ca/2015/01/a-brief-overview-of-deep-learning.html)
2. [Tomasz Malisiewicz: From Feature Descriptors to Deep Learning] (http://quantombone.blogspot.com/2015/01/from-feature-descriptors-to-deep.html)
3. [Christopher Olah: Visualizing Representations: Deep Learning and Human Beings] (http://colah.github.io/posts/2015-01-Visualizing-Representations/)
4. [Nikhil Buduma: A Deep Dive into Recurrent Neural Nets] (http://nikhilbuduma.com/2015/01/11/a-deep-dive-into-recurrent-neural-networks/)
5. [Andrej Karpathy: What I Learned From Competing Against A ConvNet On ImageNet] (http://karpathy.github.io/2014/09/02/what-i-learned-from-competing-against-a-convnet-on-imagenet/)
6. [Andrej Karpathy: On surpassing human-level performance on ImageNet] (https://plus.google.com/+AndrejKarpathy/posts/dwDNcBuWTWf)
7. Andrej Karpathy: Hacker's guide to Neural Networks
8. [C3D: Generic Features for Video Analysis] (https://research.facebook.com/blog/736987489723834/c3d-generic-features-for-video-analysis)

• ImageNet Classification with Deep Convolutional Neural Networks
• [Deep Image (the 6.0% paper)] (http://arxiv.org/abs/1501.02876)
• [Delving Deep into Rectifiers (the 4.94% paper)] (http://arxiv-web3.library.cornell.edu/pdf/1502.01852v1.pdf)
• [Batch Normalization (the 4.8% paper)] (http://arxiv.org/abs/1502.03167)

• Using Very Deep Autoencoders for Content Based Image Retrieval
• Learning Deep Architectures for AI
• Training tricks by YB
• Unsupervised Learning of Video Representations using LSTMs

• [Text Understanding From Scratch] (http://arxiv.org/abs/1502.01710)
• [DeViSE: A Deep Visual-Semantic Embedding Model] (http://research.google.com/pubs/pub41473.html)

• [Memory Networks] (http://arxiv.org/abs/1410.3916)
• [Neural Turing Machines] (http://arxiv.org/abs/1410.5401v1)
• [Learning To Execute] (http://arxiv.org/abs/1410.4615)
• DRAW: A Recurrent Neural Network For Image Generation
• Unsupervised Learning of Video Representations using LSTMs
• Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks

• [Show and Tell: A Neural Image Caption Generator] (http://arxiv.org/abs/1411.4555)
• [Show, Attend, and Tell: Neural Image Caption Generation with Visual Attention] (http://arxiv.org/abs/1502.03044)

• [From Pixels to Torques] (http://arxiv.org/abs/1502.02251)
• [Evolutionary Artificial Neural Network Based on Chemical Reaction Optimization] (http://arxiv.org/abs/1502.00193)
• [Collaborative Feature Learning] (http://arxiv.org/pdf/1502.01423v1.pdf)
• [Abstract Learning via Demodulation in a Deep Neural Network] (http://arxiv.org/abs/1502.04042v1)
• Dropout: A Simple Way to Prevent Neural Networks from Overfitting

• [Geoff Hinton's reading list (all papers)] (http://www.cs.toronto.edu/~hinton/deeprefs.html)
• [NIPS 2014 - accepted papers] (http://www.dlworkshop.org/accepted-papers)
• CMU’s list of papers

1. Deep Learning by Yoshua Bengio, Ian Goodfellow and Aaron Courville (01/01/2015)
2. Neural Networks and Deep Learning by Michael Nielsen (Dec 2014)
3. Deep Learning by Microsoft Research (2013)
4. Deep Learning Tutorial by LISA lab, University of Montreal (Jan 6 2015)
5. An introduction to genetic algorithms
6. Artificial Intelligence: A Modern Approach

1. Machine Learning - Stanford by Andrew Ng in Coursera (2010-2014)
2. Machine Learning - Caltech by Yaser Abu-Mostafa (2012-2014)
3. Machine Learning - Carnegie Mellon by Tom Mitchell (Spring 2011)
4. Neural Networks for Machine Learning by Geoffrey Hinton in Coursera (2012)
5. Neural networks class by Hugo Larochelle from Université de Sherbrooke (2013)
6. Deep Learning Course by CILVR lab @ NYU (2014)
7. A.I - Berkeley by Dan Klein and Pieter Abbeel (2013)
8. A.I - MIT by Patrick Henry Winston (2010)
9. Vision and learning - computers and brains by Shimon Ullman, Tomaso Poggio, Ethan Meyers @ MIT (2013)
10. Convolutional Neural Networks for Visual Recognition - Stanford by Fei-Fei Li, Andrej Karpathy (2015)
11. Neural Networks for Named Entity Recognition zip

1. How To Create A Mind By Ray Kurzweil
2. Deep Learning, Self-Taught Learning and Unsupervised Feature Learning By Andrew Ng
3. Recent Developments in Deep Learning By Geoff Hinton
4. The Unreasonable Effectiveness of Deep Learning by Yann LeCun
5. Deep Learning of Representations by Yoshua bengio
6. Principles of Hierarchical Temporal Memory by Jeff Hawkins
7. Machine Learning Discussion Group - Deep Learning w/ Stanford AI Lab by Adam Coates
8. Making Sense of the World with Deep Learning By Adam Coates
9. Demystifying Unsupervised Feature Learning By Adam Coates
10. Visual Perception with Deep Learning By Yann LeCun
11. The Next Generation of Neural Networks By Geoffrey Hinton at GoogleTechTalks
12. The wonderful and terrifying implications of computers that can learn By Jeremy Howard at TEDxBrussels
13. Unsupervised Deep Learning - Stanford by Andrew Ng in Stanford (2011)
14. [Natural Language Processing] (http://web.stanford.edu/class/cs224n/handouts/) By Chris Manning in Stanford
15. Google's Large Scale Deep Neural Networks Project
16. Automated Image Captioning with ConvNets and Recurrent Nets

1. UFLDL Tutorial 1
2. UFLDL Tutorial 2
3. Deep Learning for NLP (without Magic)
4. A Deep Learning Tutorial: From Perceptrons to Deep Networks
5. Deep Learning from the Bottom up
6. Theano Tutorial
7. Neural Networks for Matlab
8. Using convolutional neural nets to detect facial keypoints tutorial
9. Torch7 Tutorials

1. deeplearning.net
2. deeplearning.stanford.edu
3. nlp.stanford.edu
4. ai-junkie.com
5. cs.brown.edu/research/ai
6. eecs.umich.edu/ai
7. cs.utexas.edu/users/ai-lab
8. cs.washington.edu/research/ai
9. aiai.ed.ac.uk
10. www-aig.jpl.nasa.gov
11. csail.mit.edu
12. cgi.cse.unsw.edu.au/~aishare
13. cs.rochester.edu/research/ai
14. ai.sri.com
15. isi.edu/AI/isd.htm
16. nrl.navy.mil/itd/aic
17. hips.seas.harvard.edu

1. MNIST Handwritten digits
2. Google House Numbers from street view
3. CIFAR-10 and CIFAR-1004.
4. IMAGENET
5. Tiny Images 80 Million tiny images6.
6. Flickr Data 100 Million Yahoo dataset
7. Berkeley Segmentation Dataset 500
8. UC Irvine Machine Learning Repository

1. Caffe
2. Torch7
3. Theano
4. cuda-convnet
5. convetjs
6. Ccv
7. NuPIC
8. DeepLearning4J
9. Brain
10. DeepLearnToolbox
11. Deepnet
12. Deeppy
13. JavaNN
14. hebel
15. Mocha.jl
16. OpenDL
17. cuDNN

1. Google Plus - Deep Learning Community
2. Caffe Webinar
3. 100 Best Github Resources in Github for DL
4. Word2Vec
5. Caffe DockerFile
6. TorontoDeepLEarning convnet
7. Vision data sets
8. gfx.js
9. Torch7 Cheat sheet
10. [Misc from MIT's 'Advanced Natural Language Processing' course] (http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-864-advanced-natural-language-processing-fall-2005/)
11. Misc from MIT's 'Machine Learning' course
12. Misc from MIT's 'Networks for Learning: Regression and Classification' course
13. Misc from MIT's 'Neural Coding and Perception of Sound' course
14. Implementing a Distributed Deep Learning Network over Spark
15. A chess AI that learns to play chess using deep learning.
16. [Reproducing the results of "Playing Atari with Deep Reinforcement Learning" by DeepMind] (https://github.com/kristjankorjus/Replicating-DeepMind)
17. [Torch vs. Theano] (http://fastml.com/torch-vs-theano/)

1. [Image Kernels Explained Visually] (http://setosa.io/ev/image-kernels/)
2. [Explained Visually] (http://setosa.io/ev/)
3. [Visualizing Algorithms] (http://bost.ocks.org/mike/algorithms/)

Have anything in mind that you think is awesome and would fit in this list? Feel free to send a pull request.